Analyse and recommend
Poiesis
AI-powered recommendations and strategic insights — a customisable analytics engine that forms the foundation of the Datatronika offering.
Position
Poiesis does not stop at what the numbers say. It holds context, states objectives, and returns recommendations operators can act on — with the reasoning path intact.
Deploy as a standalone analytical surface or integrate with the infrastructure you already trust. Either way, it is designed to sit on connected data and hand explainable output downstream to Praxis Actions.
What it returns
- 01
Analytics
Patterns and structure in the data that would otherwise stay latent in siloed reports.
- 02
Hypotheses
Testable, data-grounded theories about the business — explicit enough to challenge or refine.
- 03
Recommendations
Ranked next actions with reasoning attached — what the data means, and what to do.
Capabilities
Built for depth, not dashboard theatre
The surface is analytical. The system underneath is modular agents, governed integrations and reasoning you can inspect.
Modular deployment
Stand alone or sit on the warehouses, lakes and APIs you already run — architecture that bends to the estate, not the other way around.
Analytical prompts and agents
Hundreds of generated analytical prompts and functions, plus a standard set of agents teams can extend for their own decision vocabulary.
AI and machine learning
Models and language systems applied where they earn their place — not as spectacle, but as instruments inside a governed analytical path.
Batch and real-time
Processing matched to the decision clock: continuous interrogation when latency matters, scheduled runs when it does not.
Reasoning visualisation
LLM chain-of-thought rendered as analytical graphs — the prompt chain and evidence path made inspectable, not buried in a black box.
BI and data platform fit
Native adjacency to Power BI, Tableau, Qlik, Looker, Databricks, databases and lakes — Poiesis reads the estate; it does not replace it by default.
Multi-agent system
Specialists in concert — not one model pretending to do everything
At the core of Poiesis is a network of agents that share intermediate insight. Statistics, goals, analysis and recommendation stay distinct jobs — then compose.
Input
Questions, business context, market signal, briefs, research and internal documents — ingested alongside operational data so analysis starts from the landscape, not a single table.
Multi-agent processing
Specialised agents handle distinct analytical jobs and share intermediate results. No single model pretends to own the whole problem.
Data integration
Seamless reach into existing infrastructure and APIs — BI tools, warehouses, lakes and operational systems — so depth of analysis matches depth of available evidence.
Collaborative intelligence
Agents for statistics, goals, analysis and recommendation work in concert: context is held, objectives are stated, nuance survives the aggregation.
In the system
Connect. Build. Analyse. Act.
Poiesis sits on top of connected, engineered data and hands its output to Praxis Actions.
Next step
Bring the decision Poiesis should own
Show us the question that currently ends in a slide deck or a contested spreadsheet. We will map the agents, evidence path and recommendation shape around it.